rANOMALY use case
Etienne Rifa & Sebastien Theil
17/08/2020
1 rANOMALY step-by-step use case.
1.1 Help
Each function have a detailed help accessible in R via ?{funtion}.
1.2 Tests datasets
The dataset can be downloaded via this link.
This tutorial assume that you have extracted all the read file in a folder named reads along with the sample-metadata.csv file.
We share a 24 samples test dataset extract from rats feces at two different time (t0 & t50) and in two nutrition conditions. Also included two extraction control sample (blank).
sm <- read.table("sample_metadata.csv", sep="\t",header=TRUE)
DT::datatable(sm)load("decontam_out/robjects.Rdata")1.3 ASV definition with DADA2
The first step will be the creation of ASVs (Amplicon Sequence Variants) thanks to the dada2 package. In rANOMALY, only one function is needed to compute all the different steps require from this package.
Sample names will be extracted from the file name, so files must be formatted as followed : {sample-id1}_R1.fastq.gz {sample-id1}_R2.fastq.gz etc…
dada_res = dada2_fun(path="./reads", dadapool = "pseudo", compress=TRUE, plot=FALSE)Main output: - read_tracking.csv that summarize the read number after each filtering step.
DT::datatable(read.table("dada2_out/read_tracking.csv",sep="\t",header=TRUE))The sample names extracted from the file name. We consider as sample name anything that is before the first underscore. This must match the sample names that are in sample metadata files. input: raw read number. filtered: after dada2 filtering step: no N’s in sequence, low quality, and phiX. denoisedF & denoisedR: after denoising. Forward & Reverse. merged: after merging R1 & R2. nonchim: after chimeras filtering.
dada2_robjects.Rdatawith raw ASV table and representative sequences in objectsotu.table,seqtab.export&seqtab.nochim.raw_asv-table.csvrep-seqs.fna
1.4 Taxonomic assignment
This function uses IDTAXA function from DECIPHER package, and allows to use 2 differents databases. It keeps the best assignation on 2 criteria, resolution (depth) and confidence. The final taxonomy is validated by multiple ancestors taxa and incongruity correction step.
We share the latest databases we use in the IDTAXA format in this link. You can also generate your own database following those instructions and scripts we provide in another repository.
tax.table = assign_taxo_fun(dada_res = dada_res, id_db = c("path_to_your_banks/silva/SILVA_SSU_r132_March2018.RData","path_to_your_banks/DAIRYdb_v1.2.0_20190222_IDTAXA.RData") )Main output: - taxo_robjects.Rdata with taxonomy in phyloseq format in tax.table object. - final_tax_table.csv the final assignation table that will be use in next steps. - allDB_tax_table.csv raw assignations from the two databases, mainly for debugging.
1.5 Phylogenetic Tree
The phylogenetic tree from the representative sequences is generated using phangorn and DECIPHER packages.
tree = generate_tree_fun(dada_res)Main output: - tree_robjects.Rdata with phylogenetic tree object in phyloseq format.
1.6 Phyloseq object
To create a phyloseq object, we need to merge four objects and one file: - the asv table otu.table and the representative sequences seqtab.nochim from dada2_robjects.Rdata - a taxonomy table taxo_robjects.Rdata from taxo_robjects.Rdata - the phylogenetic tree tree from tree_robjects.Rdata - metadata from sample-metadata.csv
data = generate_phyloseq_fun(dada_res = dada_res, taxtable = tax.table, tree = tree, metadata = "./sample_metadata.csv")Main output: - robjects.Rdata with phyloseq object in data for raw counts and data_rel for relative abundance.
1.7 Decontamination
The decontam_fun function uses decontam R package with control samples to filter contaminants. The decontam package offers two main methods, frequency and prevalence (and then you can combine those methods). For frequency method, it is mandatory to have the dna concentration of each sample in phyloseq (and hence in the sample-metadata.csv). “In this method, the distribution of the frequency of each sequence feature as a function of the input DNA concentration is used to identify contaminants.” In the prevalence methods no need of DNA quantification. “In this method, the prevalence (presence/absence across samples) of each sequence feature in true positive samples is compared to the prevalence in negative controls to identify contaminants.”
Tips: sequencing plateforms often quantify the DNA before sequencing, but do not automaticaly give the information. Just ask for it ;).
Our function integrates the basics ASV frequency (nb_reads_ASV/nb_total_reads) and prevalence (nb_sample_ASV/nb_total_sample) filtering. As in our lab we had a known recurrent contaminant we included an option to filter out ASV based on they taxa names.
data = decontam_fun(data = data, domain = "Bacteria", column = "type", ctrl_identifier = "control", spl_identifier = "sample", number = 100)Main output: - robjects.Rdata with contaminant filtered phyloseq object named data. - Exclu_out.csv list of filtered ASVs for each filtering step. - Kronas before and after filtering. - raw_asv-table.csv & relative_asv-table.csv. - venndiag_filtering.png.
venndiag
1.8 Plots, diversity and statistics
!!! We are currently developping a ShinyApp to visualize your data, sub-select your samples/taxons and do all those analyses interactively !!! ExploreMetabar
1.8.1 Rarefaction curves
In order to observe the sampling depth of each samples we start by plotting rarefactions curves. Those plots are generated by Plotly which makes the plots interactive.
rarefaction(data, "souche_temps", 100 )## rarefying sample SB1-Sauv0
## rarefying sample SB10-Mut0
## rarefying sample SB11-Mut0
## rarefying sample SB12-Mut0
## rarefying sample SB13-Sauv50
## rarefying sample SB14-Sauv50
## rarefying sample SB15-Sauv50
## rarefying sample SB16-Sauv50
## rarefying sample SB17-Sauv50
## rarefying sample SB18-Sauv50
## rarefying sample SB19-Mut50
## rarefying sample SB2-Sauv0
## rarefying sample SB20-Mut50
## rarefying sample SB21-Mut50
## rarefying sample SB22-Mut50
## rarefying sample SB23-Mut50
## rarefying sample SB24-Mut50
## rarefying sample SB3-Sauv0
## rarefying sample SB4-Sauv0
## rarefying sample SB5-Sauv0
## rarefying sample SB6-Sauv0
## rarefying sample SB7-Mut0
## rarefying sample SB8-Mut0
## rarefying sample SB9-Mut0## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.1.8.2 Composition plots
Composition plots reveals here the top 10 genus present in our samples. #TODO Ord1 option control the… Fact1 option control the…
1.8.2.1 Relative abundance
bars_fun(data = data, top = 10, Ord1 = "souche_temps", Fact1 = "souche_temps", rank="Genus", relative = TRUE)1.8.2.2 Raw abundance
bars_fun(data = data, top = 10, Ord1 = "souche_temps", Fact1 = "souche_temps", rank="Genus", relative = FALSE)1.8.3 Diversity analyses
1.8.3.1 Alpha diversity
This function computes various alpha diversity indexes and returns
alpha <- diversity_alpha_fun(data = data, output = "./plot_div_alpha/", column1 = "souche", column2 = "temps",
column3 = "", supcovs = "", measures = c("Observed", "Shannon") )## INFO [2020-08-20 11:43:20] Alpha diversity tab ...
## INFO [2020-08-20 11:43:20] Done.
## INFO [2020-08-20 11:43:20] Plotting ...
## INFO [2020-08-20 11:43:20] Done.## INFO [2020-08-20 11:43:21] ANOVA ...
## INFO [2020-08-20 11:43:21] Done.
## INFO [2020-08-20 11:43:21] Finish.1.8.3.1.1 Tables
pander(alpha$alphatable, style='rmarkdown')| Observed | Shannon | |
|---|---|---|
| SB1.Sauv0 | 41 | 1.477 |
| SB10.Mut0 | 40 | 2.073 |
| SB11.Mut0 | 51 | 2.178 |
| SB12.Mut0 | 38 | 2.116 |
| SB13.Sauv50 | 46 | 2.691 |
| SB14.Sauv50 | 57 | 2.905 |
| SB15.Sauv50 | 50 | 2.793 |
| SB16.Sauv50 | 52 | 2.8 |
| SB17.Sauv50 | 49 | 2.624 |
| SB18.Sauv50 | 54 | 2.831 |
| SB19.Mut50 | 66 | 2.638 |
| SB2.Sauv0 | 26 | 2.099 |
| SB20.Mut50 | 72 | 2.721 |
| SB21.Mut50 | 79 | 3.062 |
| SB22.Mut50 | 81 | 2.81 |
| SB23.Mut50 | 84 | 3.175 |
| SB24.Mut50 | 90 | 3.148 |
| SB3.Sauv0 | 19 | 0.1962 |
| SB4.Sauv0 | 41 | 2.52 |
| SB5.Sauv0 | 46 | 1.923 |
| SB6.Sauv0 | 46 | 1.067 |
| SB7.Mut0 | 33 | 2.256 |
| SB8.Mut0 | 58 | 2.089 |
| SB9.Mut0 | 50 | 2.237 |
1.8.3.2 Boxplots
alpha$plot
1.8.3.3 ANOVA results
pander(alpha$anova)| Df | Sum Sq | Mean Sq | F value | Pr(>F) | |
|---|---|---|---|---|---|
| Depth | 1 | 1.777 | 1.777 | 10.1 | 0.004731 |
| souche | 1 | 0.52 | 0.52 | 2.955 | 0.101 |
| temps | 1 | 5.141 | 5.141 | 29.22 | 2.727e-05 |
| Residuals | 20 | 3.519 | 0.176 | NA | NA |
beta <- diversity_beta_fun(data = data, output = "./plot_div_beta/", glom = "ASV", column1 = "temps", column2 = "souche", covar ="")## INFO [2020-08-20 11:43:22] Option1...
## [1] "t0" "t50"
## INFO [2020-08-20 11:43:22] Split table t0...
## INFO [2020-08-20 11:43:22] Done.
## [1] ""
## INFO [2020-08-20 11:43:22] No glom ...
## INFO [2020-08-20 11:43:22] Bray ...
##
## mutant sauvage
## 6 6
## INFO [2020-08-20 11:43:23] Done
## INFO [2020-08-20 11:43:23] Unifrac ...
## INFO [2020-08-20 11:43:23] Done
## INFO [2020-08-20 11:43:23] wunifrac ...
## INFO [2020-08-20 11:43:23] Done
##
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.53973 0.53973 2.8355 0.17954 0.015984 *
## souche 1 0.75338 0.75338 3.9580 0.25061 0.003996 **
## Residuals 9 1.71311 0.19035 0.56985
## Total 11 3.00623 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.952842 4.640344 0.3169559 0.005 0.005 *
##
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.12045 0.120447 1.6362 0.12272 0.134865
## souche 1 0.19850 0.198504 2.6965 0.20225 0.005994 **
## Residuals 9 0.66253 0.073615 0.67503
## Total 11 0.98148 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.2429196 3.289082 0.2475026 0.003 0.003 *
##
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.51694 0.51694 5.3962 0.32059 0.000999 ***
## souche 1 0.23337 0.23337 2.4360 0.14472 0.068931 .
## Residuals 9 0.86218 0.09580 0.53469
## Total 11 1.61249 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.3815338 3.099498 0.236612 0.048 0.048 .
## INFO [2020-08-20 11:43:23] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1383252
## Run 1 stress 0.1383248
## ... New best solution
## ... Procrustes: rmse 0.0007111882 max resid 0.001641181
## ... Similar to previous best
## Run 2 stress 0.1383248
## ... Procrustes: rmse 5.18931e-05 max resid 0.0001092668
## ... Similar to previous best
## Run 3 stress 0.2136661
## Run 4 stress 0.1415946
## Run 5 stress 0.1383252
## ... Procrustes: rmse 0.0003841452 max resid 0.0008622343
## ... Similar to previous best
## Run 6 stress 0.1383256
## ... Procrustes: rmse 0.0005469171 max resid 0.00126535
## ... Similar to previous best
## Run 7 stress 0.221726
## Run 8 stress 0.1415932
## Run 9 stress 0.1415945
## Run 10 stress 0.2116422
## Run 11 stress 0.141596
## Run 12 stress 0.1383263
## ... Procrustes: rmse 0.0008088911 max resid 0.001955122
## ... Similar to previous best
## Run 13 stress 0.138326
## ... Procrustes: rmse 0.000676254 max resid 0.00159838
## ... Similar to previous best
## Run 14 stress 0.1383279
## ... Procrustes: rmse 0.0006174498 max resid 0.00120667
## ... Similar to previous best
## Run 15 stress 0.1383254
## ... Procrustes: rmse 0.0004564917 max resid 0.001059926
## ... Similar to previous best
## Run 16 stress 0.1471255
## Run 17 stress 0.1383255
## ... Procrustes: rmse 0.0008526309 max resid 0.001598426
## ... Similar to previous best
## Run 18 stress 0.138326
## ... Procrustes: rmse 0.0007330918 max resid 0.001713313
## ... Similar to previous best
## Run 19 stress 0.1383254
## ... Procrustes: rmse 0.0004549941 max resid 0.001127637
## ... Similar to previous best
## Run 20 stress 0.1415929
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1383248
## Run 1 stress 0.138326
## ... Procrustes: rmse 0.00116427 max resid 0.002224766
## ... Similar to previous best
## Run 2 stress 0.1383257
## ... Procrustes: rmse 0.0008774465 max resid 0.001741337
## ... Similar to previous best
## Run 3 stress 0.1383257
## ... Procrustes: rmse 0.0007999315 max resid 0.001834499
## ... Similar to previous best
## Run 4 stress 0.1416676
## Run 5 stress 0.2172085
## Run 6 stress 0.2172085
## Run 7 stress 0.1383266
## ... Procrustes: rmse 0.001330379 max resid 0.002410997
## ... Similar to previous best
## Run 8 stress 0.141673
## Run 9 stress 0.1383253
## ... Procrustes: rmse 0.0006400523 max resid 0.001466764
## ... Similar to previous best
## Run 10 stress 0.1416629
## Run 11 stress 0.1416388
## Run 12 stress 0.1415953
## Run 13 stress 0.1416056
## Run 14 stress 0.138325
## ... Procrustes: rmse 0.0006067849 max resid 0.001377457
## ... Similar to previous best
## Run 15 stress 0.1415934
## Run 16 stress 0.1415938
## Run 17 stress 0.1383253
## ... Procrustes: rmse 0.0007010871 max resid 0.001357053
## ... Similar to previous best
## Run 18 stress 0.1383299
## ... Procrustes: rmse 0.002229527 max resid 0.004072661
## ... Similar to previous best
## Run 19 stress 0.1383263
## ... Procrustes: rmse 0.001282458 max resid 0.002347546
## ... Similar to previous best
## Run 20 stress 0.1415945
## *** Solution reached
## Run 0 stress 0.1396049
## Run 1 stress 0.1554503
## Run 2 stress 0.1396049
## ... Procrustes: rmse 5.171482e-05 max resid 0.0001105483
## ... Similar to previous best
## Run 3 stress 0.1396049
## ... Procrustes: rmse 3.297584e-06 max resid 8.219886e-06
## ... Similar to previous best
## Run 4 stress 0.2416939
## Run 5 stress 0.2417539
## Run 6 stress 0.139605
## ... Procrustes: rmse 0.000201458 max resid 0.0004305822
## ... Similar to previous best
## Run 7 stress 0.1396049
## ... Procrustes: rmse 5.829218e-05 max resid 0.0001225176
## ... Similar to previous best
## Run 8 stress 0.1554507
## Run 9 stress 0.1396049
## ... Procrustes: rmse 0.0001164561 max resid 0.000248645
## ... Similar to previous best
## Run 10 stress 0.1396049
## ... Procrustes: rmse 5.608239e-05 max resid 0.0001195182
## ... Similar to previous best
## Run 11 stress 0.1396049
## ... New best solution
## ... Procrustes: rmse 2.895523e-05 max resid 6.0345e-05
## ... Similar to previous best
## Run 12 stress 0.1554503
## Run 13 stress 0.139605
## ... Procrustes: rmse 0.0001537489 max resid 0.0003312175
## ... Similar to previous best
## Run 14 stress 0.1396049
## ... Procrustes: rmse 3.796088e-05 max resid 6.718204e-05
## ... Similar to previous best
## Run 15 stress 0.1396049
## ... Procrustes: rmse 5.928586e-05 max resid 0.0001253705
## ... Similar to previous best
## Run 16 stress 0.2524046
## Run 17 stress 0.1396049
## ... New best solution
## ... Procrustes: rmse 1.214634e-05 max resid 2.424074e-05
## ... Similar to previous best
## Run 18 stress 0.1396049
## ... Procrustes: rmse 5.139167e-05 max resid 0.000108197
## ... Similar to previous best
## Run 19 stress 0.3075082
## Run 20 stress 0.2748174
## *** Solution reached
## Run 0 stress 0.04595665
## Run 1 stress 0.08158394
## Run 2 stress 0.08294192
## Run 3 stress 0.0837327
## Run 4 stress 0.05319186
## Run 5 stress 0.08157902
## Run 6 stress 0.05106463
## Run 7 stress 0.05318663
## Run 8 stress 0.04595632
## ... New best solution
## ... Procrustes: rmse 9.027851e-05 max resid 0.000221114
## ... Similar to previous best
## Run 9 stress 0.05317947
## Run 10 stress 0.3236485
## Run 11 stress 0.04595618
## ... New best solution
## ... Procrustes: rmse 0.0009933373 max resid 0.002432997
## ... Similar to previous best
## Run 12 stress 0.05106899
## Run 13 stress 0.08294091
## Run 14 stress 0.08157455
## Run 15 stress 0.05317786
## Run 16 stress 0.04595625
## ... Procrustes: rmse 2.226313e-05 max resid 4.693294e-05
## ... Similar to previous best
## Run 17 stress 0.08256781
## Run 18 stress 0.08294084
## Run 19 stress 0.08256771
## Run 20 stress 0.04595598
## ... New best solution
## ... Procrustes: rmse 7.318793e-05 max resid 0.00017893
## ... Similar to previous best
## *** Solution reached
## INFO [2020-08-20 11:43:24] Done.
## INFO [2020-08-20 11:43:24] Saving ...## INFO [2020-08-20 11:43:26] Supplement Beta plots ...
## INFO [2020-08-20 11:43:26] Done.
## INFO [2020-08-20 11:43:26] Split table t50...
## INFO [2020-08-20 11:43:26] Done.
## [1] ""
## INFO [2020-08-20 11:43:26] No glom ...
## INFO [2020-08-20 11:43:26] Bray ...
##
## mutant sauvage
## 6 6
## INFO [2020-08-20 11:43:26] Done
## INFO [2020-08-20 11:43:26] Unifrac ...
## INFO [2020-08-20 11:43:26] Done
## INFO [2020-08-20 11:43:26] wunifrac ...
## INFO [2020-08-20 11:43:26] Done
##
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.06369 0.06369 3.118 0.03093 0.106893
## souche 1 1.81185 1.81185 88.707 0.87981 0.000999 ***
## Residuals 9 0.18383 0.02043 0.08926
## Total 11 2.05937 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 1.817719 75.21929 0.8826557 0.005 0.005 *
##
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.08744 0.08744 6.044 0.08564 0.010989 *
## souche 1 0.80336 0.80336 55.529 0.78683 0.000999 ***
## Residuals 9 0.13021 0.01447 0.12753
## Total 11 1.02100 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.8054894 37.37546 0.7889203 0.004 0.004 *
##
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.005527 0.005527 2.215 0.02294 0.146853
## souche 1 0.212963 0.212963 85.338 0.88385 0.000999 ***
## Residuals 9 0.022460 0.002496 0.09321
## Total 11 0.240949 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.2133365 77.26041 0.8854005 0.001 0.001 **
## INFO [2020-08-20 11:43:26] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 7.297422e-05
## Run 1 stress 9.072339e-05
## ... Procrustes: rmse 7.768677e-05 max resid 0.0002166727
## ... Similar to previous best
## Run 2 stress 0.2334764
## Run 3 stress 9.005844e-05
## ... Procrustes: rmse 9.739143e-05 max resid 0.0001717861
## ... Similar to previous best
## Run 4 stress 9.95859e-05
## ... Procrustes: rmse 9.322439e-05 max resid 0.0002091992
## ... Similar to previous best
## Run 5 stress 8.897427e-05
## ... Procrustes: rmse 0.0002216845 max resid 0.0005575271
## ... Similar to previous best
## Run 6 stress 9.832949e-05
## ... Procrustes: rmse 0.0001241593 max resid 0.0002434063
## ... Similar to previous best
## Run 7 stress 9.452182e-05
## ... Procrustes: rmse 0.0002364735 max resid 0.0005866198
## ... Similar to previous best
## Run 8 stress 9.791438e-05
## ... Procrustes: rmse 0.000263323 max resid 0.0006040692
## ... Similar to previous best
## Run 9 stress 9.296734e-05
## ... Procrustes: rmse 0.0002500079 max resid 0.0005780813
## ... Similar to previous best
## Run 10 stress 8.761903e-05
## ... Procrustes: rmse 0.0002291521 max resid 0.0005350754
## ... Similar to previous best
## Run 11 stress 9.774804e-05
## ... Procrustes: rmse 0.0002406461 max resid 0.0005989343
## ... Similar to previous best
## Run 12 stress 8.903352e-05
## ... Procrustes: rmse 7.276306e-05 max resid 0.0001593281
## ... Similar to previous best
## Run 13 stress 8.927059e-05
## ... Procrustes: rmse 6.852359e-05 max resid 0.0001890088
## ... Similar to previous best
## Run 14 stress 8.472066e-05
## ... Procrustes: rmse 8.880295e-05 max resid 0.0001598673
## ... Similar to previous best
## Run 15 stress 9.494667e-05
## ... Procrustes: rmse 0.0002560394 max resid 0.0005910742
## ... Similar to previous best
## Run 16 stress 9.92707e-05
## ... Procrustes: rmse 0.0002447724 max resid 0.000607732
## ... Similar to previous best
## Run 17 stress 9.921249e-05
## ... Procrustes: rmse 0.0001916681 max resid 0.0004861638
## ... Similar to previous best
## Run 18 stress 8.928482e-05
## ... Procrustes: rmse 8.723281e-05 max resid 0.0001592515
## ... Similar to previous best
## Run 19 stress 7.930669e-05
## ... Procrustes: rmse 8.405353e-05 max resid 0.0001571251
## ... Similar to previous best
## Run 20 stress 9.108848e-05
## ... Procrustes: rmse 0.0001178203 max resid 0.0002635204
## ... Similar to previous best
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 8.694326e-05
## Run 1 stress 9.748641e-05
## ... Procrustes: rmse 6.765283e-05 max resid 0.0001697715
## ... Similar to previous best
## Run 2 stress 9.581008e-05
## ... Procrustes: rmse 0.0002451187 max resid 0.0006773339
## ... Similar to previous best
## Run 3 stress 9.023233e-05
## ... Procrustes: rmse 2.273242e-05 max resid 4.192331e-05
## ... Similar to previous best
## Run 4 stress 9.042644e-05
## ... Procrustes: rmse 0.0002239383 max resid 0.0006292496
## ... Similar to previous best
## Run 5 stress 9.397036e-05
## ... Procrustes: rmse 0.0001409961 max resid 0.0002687657
## ... Similar to previous best
## Run 6 stress 0.2890372
## Run 7 stress 9.120384e-05
## ... Procrustes: rmse 6.520305e-05 max resid 0.0001498366
## ... Similar to previous best
## Run 8 stress 9.716996e-05
## ... Procrustes: rmse 0.0002476234 max resid 0.000686722
## ... Similar to previous best
## Run 9 stress 9.643596e-05
## ... Procrustes: rmse 0.0002569899 max resid 0.0006643846
## ... Similar to previous best
## Run 10 stress 9.886299e-05
## ... Procrustes: rmse 0.0002509961 max resid 0.0006949432
## ... Similar to previous best
## Run 11 stress 9.977757e-05
## ... Procrustes: rmse 9.713435e-05 max resid 0.00021338
## ... Similar to previous best
## Run 12 stress 8.285079e-05
## ... New best solution
## ... Procrustes: rmse 7.368284e-05 max resid 0.0001614828
## ... Similar to previous best
## Run 13 stress 9.266872e-05
## ... Procrustes: rmse 6.92998e-05 max resid 0.0001830171
## ... Similar to previous best
## Run 14 stress 7.135878e-05
## ... New best solution
## ... Procrustes: rmse 6.453509e-05 max resid 0.0001604199
## ... Similar to previous best
## Run 15 stress 9.265212e-05
## ... Procrustes: rmse 0.0001433101 max resid 0.0002567619
## ... Similar to previous best
## Run 16 stress 9.894863e-05
## ... Procrustes: rmse 0.000258855 max resid 0.0006401064
## ... Similar to previous best
## Run 17 stress 0.3140362
## Run 18 stress 8.824241e-05
## ... Procrustes: rmse 6.411572e-05 max resid 0.0001786811
## ... Similar to previous best
## Run 19 stress 8.797687e-05
## ... Procrustes: rmse 0.0001144256 max resid 0.0002361331
## ... Similar to previous best
## Run 20 stress 9.520276e-05
## ... Procrustes: rmse 0.0001340272 max resid 0.0002118414
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 9.441127e-05
## Run 1 stress 9.163055e-05
## ... New best solution
## ... Procrustes: rmse 7.530837e-05 max resid 0.0001744852
## ... Similar to previous best
## Run 2 stress 9.212023e-05
## ... Procrustes: rmse 9.70487e-05 max resid 0.0001909365
## ... Similar to previous best
## Run 3 stress 8.361978e-05
## ... New best solution
## ... Procrustes: rmse 7.343488e-05 max resid 0.0001720889
## ... Similar to previous best
## Run 4 stress 9.177707e-05
## ... Procrustes: rmse 6.425788e-05 max resid 0.0001863483
## ... Similar to previous best
## Run 5 stress 9.938801e-05
## ... Procrustes: rmse 6.958948e-05 max resid 0.0001991097
## ... Similar to previous best
## Run 6 stress 9.465171e-05
## ... Procrustes: rmse 0.000120524 max resid 0.0002486309
## ... Similar to previous best
## Run 7 stress 9.643951e-05
## ... Procrustes: rmse 0.0001610937 max resid 0.0003068382
## ... Similar to previous best
## Run 8 stress 9.401982e-05
## ... Procrustes: rmse 0.0001171673 max resid 0.0002542309
## ... Similar to previous best
## Run 9 stress 9.508155e-05
## ... Procrustes: rmse 7.59953e-05 max resid 0.0001615181
## ... Similar to previous best
## Run 10 stress 9.846444e-05
## ... Procrustes: rmse 0.0001158061 max resid 0.0002788113
## ... Similar to previous best
## Run 11 stress 9.668601e-05
## ... Procrustes: rmse 0.0001885189 max resid 0.0003120034
## ... Similar to previous best
## Run 12 stress 9.581862e-05
## ... Procrustes: rmse 6.824297e-05 max resid 0.0001954106
## ... Similar to previous best
## Run 13 stress 8.994205e-05
## ... Procrustes: rmse 5.67046e-05 max resid 0.0001377605
## ... Similar to previous best
## Run 14 stress 9.748541e-05
## ... Procrustes: rmse 8.222535e-05 max resid 0.000145379
## ... Similar to previous best
## Run 15 stress 9.440691e-05
## ... Procrustes: rmse 2.80087e-05 max resid 6.010457e-05
## ... Similar to previous best
## Run 16 stress 9.964305e-05
## ... Procrustes: rmse 3.756918e-05 max resid 8.724602e-05
## ... Similar to previous best
## Run 17 stress 9.597292e-05
## ... Procrustes: rmse 7.730236e-05 max resid 0.0001433209
## ... Similar to previous best
## Run 18 stress 7.391807e-05
## ... New best solution
## ... Procrustes: rmse 0.0001349231 max resid 0.000338976
## ... Similar to previous best
## Run 19 stress 9.032969e-05
## ... Procrustes: rmse 0.0001761176 max resid 0.0003438619
## ... Similar to previous best
## Run 20 stress 9.955021e-05
## ... Procrustes: rmse 0.0001569559 max resid 0.0004393414
## ... Similar to previous best
## *** Solution reached
## Run 0 stress 0.00169726
## Run 1 stress 0.0002197232
## ... New best solution
## ... Procrustes: rmse 0.009379533 max resid 0.01793379
## Run 2 stress 0.00123796
## Run 3 stress 0.002620137
## Run 4 stress 9.97288e-05
## ... New best solution
## ... Procrustes: rmse 0.0008186464 max resid 0.001464357
## ... Similar to previous best
## Run 5 stress 9.683841e-05
## ... New best solution
## ... Procrustes: rmse 6.559068e-05 max resid 0.0001697254
## ... Similar to previous best
## Run 6 stress 0.002604824
## Run 7 stress 0.002923216
## Run 8 stress 0.001933772
## Run 9 stress 0.0004806639
## ... Procrustes: rmse 0.002793245 max resid 0.005198637
## ... Similar to previous best
## Run 10 stress 0.001368991
## Run 11 stress 9.513837e-05
## ... New best solution
## ... Procrustes: rmse 0.0001475611 max resid 0.0003304377
## ... Similar to previous best
## Run 12 stress 0.002448876
## Run 13 stress 0.001702162
## Run 14 stress 0.00172639
## Run 15 stress 0.001758978
## Run 16 stress 0.002145436
## Run 17 stress 9.987843e-05
## ... Procrustes: rmse 0.0002861659 max resid 0.0005287277
## ... Similar to previous best
## Run 18 stress 0.001707582
## Run 19 stress 0.001591586
## Run 20 stress 0.0008198339
## *** Solution reached
## INFO [2020-08-20 11:43:27] Done.
## INFO [2020-08-20 11:43:27] Saving ...
## INFO [2020-08-20 11:43:29] Supplement Beta plots ...
## INFO [2020-08-20 11:43:29] Done.
## INFO [2020-08-20 11:43:29] Global1...
## [1] ""
## INFO [2020-08-20 11:43:29] No glom ...
## INFO [2020-08-20 11:43:29] Bray ...
## souche
## temps mutant sauvage
## t0 6 6
## t50 6 6
## INFO [2020-08-20 11:43:29] Done
## INFO [2020-08-20 11:43:29] Unifrac ...
## INFO [2020-08-20 11:43:30] Done
## INFO [2020-08-20 11:43:30] wunifrac ...
## INFO [2020-08-20 11:43:30] Done
##
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.5075 0.50751 3.1218 0.06845 0.016983 *
## temps 1 2.1846 2.18458 13.4380 0.29463 0.000999 ***
## souche 1 1.4711 1.47112 9.0493 0.19841 0.000999 ***
## Residuals 20 3.2514 0.16257 0.43851
## Total 23 7.4146 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted
## 1 t0-sauvage vs t0-mutant 1 0.952842 4.640344 0.3169559 0.003 0.0048
## 2 t0-sauvage vs t50-sauvage 1 2.020676 28.967360 0.7433750 0.004 0.0048
## 3 t0-sauvage vs t50-mutant 1 2.197269 26.004113 0.7222540 0.003 0.0048
## 4 t0-mutant vs t50-sauvage 1 1.680832 11.591365 0.5368519 0.002 0.0048
## 5 t0-mutant vs t50-mutant 1 1.569713 9.826226 0.4956176 0.007 0.0070
## 6 t50-sauvage vs t50-mutant 1 1.817719 75.219295 0.8826557 0.004 0.0048
## sig
## 1 *
## 2 *
## 3 *
## 4 *
## 5 *
## 6 *
##
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.13746 0.13746 2.4027 0.04730 0.050949 .
## temps 1 0.99444 0.99444 17.3818 0.34220 0.000999 ***
## souche 1 0.62986 0.62986 11.0092 0.21674 0.000999 ***
## Residuals 20 1.14423 0.05721 0.39375
## Total 23 2.90599 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted
## 1 t0-sauvage vs t0-mutant 1 0.2240538 3.250757 0.2453261 0.004 0.0048
## 2 t0-sauvage vs t50-sauvage 1 0.5057533 12.113203 0.5477815 0.003 0.0045
## 3 t0-sauvage vs t50-mutant 1 1.0565450 23.160697 0.6984382 0.002 0.0045
## 4 t0-mutant vs t50-sauvage 1 0.6002740 13.641788 0.5770201 0.003 0.0045
## 5 t0-mutant vs t50-mutant 1 0.8527236 17.813923 0.6404678 0.003 0.0045
## 6 t50-sauvage vs t50-mutant 1 0.7802130 37.696858 0.7903426 0.005 0.0050
## sig
## 1 *
## 2 *
## 3 *
## 4 *
## 5 *
## 6 *
##
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.27641 0.27641 6.4387 0.14496 0.002997 **
## temps 1 0.44988 0.44988 10.4795 0.23593 0.000999 ***
## souche 1 0.32197 0.32197 7.4999 0.16885 0.000999 ***
## Residuals 20 0.85859 0.04293 0.45027
## Total 23 1.90684 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted
## 1 t0-sauvage vs t0-mutant 1 0.2067852 2.841008 0.2212450 0.050 0.0500
## 2 t0-sauvage vs t50-sauvage 1 0.4645524 13.418868 0.5729939 0.005 0.0072
## 3 t0-sauvage vs t50-mutant 1 0.5903252 15.585623 0.6091555 0.006 0.0072
## 4 t0-mutant vs t50-sauvage 1 0.2788751 6.969745 0.4107160 0.003 0.0072
## 5 t0-mutant vs t50-mutant 1 0.3237289 7.481746 0.4279748 0.004 0.0072
## 6 t50-sauvage vs t50-mutant 1 0.3916400 76.752213 0.8847292 0.003 0.0072
## sig
## 1 .
## 2 *
## 3 *
## 4 *
## 5 *
## 6 *
## INFO [2020-08-20 11:43:30] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004764
## Run 1 stress 0.1278068
## Run 2 stress 0.1004882
## ... Procrustes: rmse 0.005823643 max resid 0.02234724
## Run 3 stress 0.1004764
## ... Procrustes: rmse 3.821424e-05 max resid 6.889213e-05
## ... Similar to previous best
## Run 4 stress 0.1004882
## ... Procrustes: rmse 0.005819609 max resid 0.02232734
## Run 5 stress 0.1004883
## ... Procrustes: rmse 0.00579385 max resid 0.02226849
## Run 6 stress 0.1278184
## Run 7 stress 0.1282064
## Run 8 stress 0.1004764
## ... Procrustes: rmse 1.237883e-05 max resid 3.540612e-05
## ... Similar to previous best
## Run 9 stress 0.1004764
## ... Procrustes: rmse 3.985522e-05 max resid 0.0001033497
## ... Similar to previous best
## Run 10 stress 0.1004882
## ... Procrustes: rmse 0.005829569 max resid 0.02236233
## Run 11 stress 0.1004882
## ... Procrustes: rmse 0.005809934 max resid 0.02229716
## Run 12 stress 0.1004764
## ... Procrustes: rmse 2.204869e-05 max resid 5.689465e-05
## ... Similar to previous best
## Run 13 stress 0.1004764
## ... Procrustes: rmse 2.012674e-05 max resid 7.370409e-05
## ... Similar to previous best
## Run 14 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 7.810815e-06 max resid 2.141758e-05
## ... Similar to previous best
## Run 15 stress 0.1004764
## ... Procrustes: rmse 1.299291e-05 max resid 4.917161e-05
## ... Similar to previous best
## Run 16 stress 0.1004764
## ... Procrustes: rmse 6.421255e-05 max resid 0.0001871853
## ... Similar to previous best
## Run 17 stress 0.1322902
## Run 18 stress 0.1004882
## ... Procrustes: rmse 0.005807036 max resid 0.0222636
## Run 19 stress 0.1004764
## ... Procrustes: rmse 2.01287e-05 max resid 5.353975e-05
## ... Similar to previous best
## Run 20 stress 0.1004764
## ... Procrustes: rmse 2.389933e-05 max resid 6.118326e-05
## ... Similar to previous best
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004882
## Run 1 stress 0.127583
## Run 2 stress 0.1275004
## Run 3 stress 0.1004882
## ... Procrustes: rmse 1.677829e-05 max resid 2.864545e-05
## ... Similar to previous best
## Run 4 stress 0.1004882
## ... Procrustes: rmse 2.331472e-05 max resid 5.632918e-05
## ... Similar to previous best
## Run 5 stress 0.1004883
## ... Procrustes: rmse 7.361098e-05 max resid 0.0002438938
## ... Similar to previous best
## Run 6 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 0.005815704 max resid 0.0223052
## Run 7 stress 0.1004766
## ... Procrustes: rmse 9.99347e-05 max resid 0.000263001
## ... Similar to previous best
## Run 8 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 1.145559e-05 max resid 2.963108e-05
## ... Similar to previous best
## Run 9 stress 0.1278117
## Run 10 stress 0.1004764
## ... Procrustes: rmse 2.991178e-05 max resid 9.119642e-05
## ... Similar to previous best
## Run 11 stress 0.1282049
## Run 12 stress 0.1004764
## ... Procrustes: rmse 1.148337e-05 max resid 3.044424e-05
## ... Similar to previous best
## Run 13 stress 0.1004765
## ... Procrustes: rmse 8.413438e-05 max resid 0.0002228466
## ... Similar to previous best
## Run 14 stress 0.1278136
## Run 15 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 1.082832e-05 max resid 2.927359e-05
## ... Similar to previous best
## Run 16 stress 0.1278013
## Run 17 stress 0.1004882
## ... Procrustes: rmse 0.00581578 max resid 0.02231153
## Run 18 stress 0.1004768
## ... Procrustes: rmse 7.019181e-05 max resid 0.0002005493
## ... Similar to previous best
## Run 19 stress 0.133214
## Run 20 stress 0.1278085
## *** Solution reached
## Run 0 stress 0.1227056
## Run 1 stress 0.1225313
## ... New best solution
## ... Procrustes: rmse 0.01280186 max resid 0.04737191
## Run 2 stress 0.1230686
## Run 3 stress 0.1298941
## Run 4 stress 0.1487401
## Run 5 stress 0.1295963
## Run 6 stress 0.1225312
## ... New best solution
## ... Procrustes: rmse 0.000118533 max resid 0.0004953098
## ... Similar to previous best
## Run 7 stress 0.1471614
## Run 8 stress 0.124186
## Run 9 stress 0.1487399
## Run 10 stress 0.1298942
## Run 11 stress 0.1227056
## ... Procrustes: rmse 0.01280135 max resid 0.04743095
## Run 12 stress 0.1227056
## ... Procrustes: rmse 0.01280135 max resid 0.04743425
## Run 13 stress 0.1246089
## Run 14 stress 0.1484704
## Run 15 stress 0.1475442
## Run 16 stress 0.1298612
## Run 17 stress 0.1225318
## ... Procrustes: rmse 0.0002912653 max resid 0.001263143
## ... Similar to previous best
## Run 18 stress 0.1686138
## Run 19 stress 0.148097
## Run 20 stress 0.1227056
## ... Procrustes: rmse 0.01280147 max resid 0.04743224
## *** Solution reached
## Run 0 stress 0.07629315
## Run 1 stress 0.08609846
## Run 2 stress 0.08609888
## Run 3 stress 0.07677108
## ... Procrustes: rmse 0.005849335 max resid 0.02149729
## Run 4 stress 0.1007069
## Run 5 stress 0.09543231
## Run 6 stress 0.09543309
## Run 7 stress 0.07741039
## Run 8 stress 0.2063802
## Run 9 stress 0.1027422
## Run 10 stress 0.07697398
## Run 11 stress 0.08609786
## Run 12 stress 0.07676879
## ... Procrustes: rmse 0.005578383 max resid 0.02054469
## Run 13 stress 0.08158911
## Run 14 stress 0.08134273
## Run 15 stress 0.07676945
## ... Procrustes: rmse 0.00575354 max resid 0.02128685
## Run 16 stress 0.3811938
## Run 17 stress 0.0954317
## Run 18 stress 0.09983933
## Run 19 stress 0.07740946
## Run 20 stress 0.07740898
## *** No convergence -- monoMDS stopping criteria:
## 20: stress ratio > sratmax
## INFO [2020-08-20 11:43:31] Done.
## INFO [2020-08-20 11:43:31] Saving ...
## INFO [2020-08-20 11:43:33] Supplement Beta plots ...
## INFO [2020-08-20 11:43:33] Done.
## INFO [2020-08-20 11:43:33] Global2...
## [1] ""
## INFO [2020-08-20 11:43:33] No glom ...
## INFO [2020-08-20 11:43:33] Bray ...
##
## t0 t50
## 12 12
## INFO [2020-08-20 11:43:33] Done
## INFO [2020-08-20 11:43:33] Unifrac ...
## INFO [2020-08-20 11:43:34] Done
## INFO [2020-08-20 11:43:34] wunifrac ...
## INFO [2020-08-20 11:43:34] Done
##
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.5075 0.50751 2.2568 0.06845 0.056943 .
## temps 1 2.1846 2.18458 9.7144 0.29463 0.000999 ***
## Residuals 21 4.7225 0.22488 0.63692
## Total 23 7.4146 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 t0 vs t50 1 2.348965 10.20159 0.316804 0.001 0.001 **
##
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.1408 0.1408 1.6722 0.04814 0.123876
## temps 1 1.0156 1.0156 12.0618 0.34726 0.000999 ***
## Residuals 21 1.7682 0.0842 0.60460
## Total 23 2.9246 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 t0 vs t50 1 1.024072 11.85414 0.3501533 0.001 0.001 **
##
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.14305 0.143045 4.7521 0.14129 0.002997 **
## temps 1 0.23722 0.237221 7.8807 0.23432 0.000999 ***
## Residuals 21 0.63213 0.030102 0.62439
## Total 23 1.01240 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 t0 vs t50 1 0.2780463 8.329805 0.2746409 0.001 0.001 **
## INFO [2020-08-20 11:43:34] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004764
## Run 1 stress 0.1004884
## ... Procrustes: rmse 0.005778965 max resid 0.02225563
## Run 2 stress 0.1337333
## Run 3 stress 0.1282048
## Run 4 stress 0.1004882
## ... Procrustes: rmse 0.005835325 max resid 0.02240664
## Run 5 stress 0.1004764
## ... Procrustes: rmse 8.015276e-06 max resid 2.282927e-05
## ... Similar to previous best
## Run 6 stress 0.1278161
## Run 7 stress 0.1004764
## ... Procrustes: rmse 7.186298e-06 max resid 2.389786e-05
## ... Similar to previous best
## Run 8 stress 0.1004765
## ... Procrustes: rmse 3.511e-05 max resid 0.0001371272
## ... Similar to previous best
## Run 9 stress 0.1004883
## ... Procrustes: rmse 0.005842995 max resid 0.02239032
## Run 10 stress 0.1004764
## ... Procrustes: rmse 8.769913e-06 max resid 2.612497e-05
## ... Similar to previous best
## Run 11 stress 0.1282048
## Run 12 stress 0.1316111
## Run 13 stress 0.1282054
## Run 14 stress 0.1337266
## Run 15 stress 0.1282047
## Run 16 stress 0.1282046
## Run 17 stress 0.1282059
## Run 18 stress 0.1004764
## ... Procrustes: rmse 1.1402e-05 max resid 3.68951e-05
## ... Similar to previous best
## Run 19 stress 0.1278101
## Run 20 stress 0.1278163
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004882
## Run 1 stress 0.1282046
## Run 2 stress 0.1004765
## ... New best solution
## ... Procrustes: rmse 0.005817589 max resid 0.02227882
## Run 3 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 2.382594e-05 max resid 4.854205e-05
## ... Similar to previous best
## Run 4 stress 0.1004882
## ... Procrustes: rmse 0.005816155 max resid 0.02230668
## Run 5 stress 0.1004882
## ... Procrustes: rmse 0.005812883 max resid 0.02228353
## Run 6 stress 0.1278125
## Run 7 stress 0.128206
## Run 8 stress 0.1278106
## Run 9 stress 0.1004882
## ... Procrustes: rmse 0.005816782 max resid 0.0223074
## Run 10 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 4.756458e-05 max resid 0.0001282969
## ... Similar to previous best
## Run 11 stress 0.1282051
## Run 12 stress 0.1004764
## ... Procrustes: rmse 2.937284e-05 max resid 6.478681e-05
## ... Similar to previous best
## Run 13 stress 0.1282046
## Run 14 stress 0.1004882
## ... Procrustes: rmse 0.005822232 max resid 0.0223718
## Run 15 stress 0.1004882
## ... Procrustes: rmse 0.00581379 max resid 0.02232067
## Run 16 stress 0.1004764
## ... Procrustes: rmse 1.849408e-05 max resid 5.629347e-05
## ... Similar to previous best
## Run 17 stress 0.1004882
## ... Procrustes: rmse 0.005813968 max resid 0.02232192
## Run 18 stress 0.1322855
## Run 19 stress 0.1004882
## ... Procrustes: rmse 0.005806415 max resid 0.02230706
## Run 20 stress 0.1275823
## *** Solution reached
## Run 0 stress 0.1211578
## Run 1 stress 0.1211578
## ... New best solution
## ... Procrustes: rmse 1.556443e-06 max resid 4.027713e-06
## ... Similar to previous best
## Run 2 stress 0.1223139
## Run 3 stress 0.1223137
## Run 4 stress 0.1515542
## Run 5 stress 0.1223129
## Run 6 stress 0.1208049
## ... New best solution
## ... Procrustes: rmse 0.01452796 max resid 0.05164647
## Run 7 stress 0.1211578
## ... Procrustes: rmse 0.01452758 max resid 0.05175251
## Run 8 stress 0.1208049
## ... New best solution
## ... Procrustes: rmse 4.179053e-06 max resid 8.571987e-06
## ... Similar to previous best
## Run 9 stress 0.1218747
## Run 10 stress 0.1223134
## Run 11 stress 0.1211578
## ... Procrustes: rmse 0.01452878 max resid 0.05175696
## Run 12 stress 0.120805
## ... Procrustes: rmse 1.900015e-05 max resid 4.648789e-05
## ... Similar to previous best
## Run 13 stress 0.1669989
## Run 14 stress 0.1211578
## ... Procrustes: rmse 0.01452832 max resid 0.05175863
## Run 15 stress 0.1660973
## Run 16 stress 0.1211578
## ... Procrustes: rmse 0.01453113 max resid 0.0517506
## Run 17 stress 0.1208049
## ... Procrustes: rmse 9.373579e-07 max resid 2.849566e-06
## ... Similar to previous best
## Run 18 stress 0.1211578
## ... Procrustes: rmse 0.01452917 max resid 0.05175842
## Run 19 stress 0.1211578
## ... Procrustes: rmse 0.01452834 max resid 0.05175799
## Run 20 stress 0.1220804
## *** Solution reached
## Run 0 stress 0.0911341
## Run 1 stress 0.08179215
## ... New best solution
## ... Procrustes: rmse 0.0888207 max resid 0.3492943
## Run 2 stress 0.09165344
## Run 3 stress 0.09113433
## Run 4 stress 0.09165304
## Run 5 stress 0.09113439
## Run 6 stress 0.08913187
## Run 7 stress 0.09236561
## Run 8 stress 0.08179264
## ... Procrustes: rmse 0.0002713964 max resid 0.0009849656
## ... Similar to previous best
## Run 9 stress 0.07961571
## ... New best solution
## ... Procrustes: rmse 0.1153149 max resid 0.2288016
## Run 10 stress 0.08915446
## Run 11 stress 0.09113402
## Run 12 stress 0.08179247
## Run 13 stress 0.08023629
## Run 14 stress 0.3841428
## Run 15 stress 0.09731315
## Run 16 stress 0.08179313
## Run 17 stress 0.07961325
## ... New best solution
## ... Procrustes: rmse 0.0007558797 max resid 0.001625623
## ... Similar to previous best
## Run 18 stress 0.0817938
## Run 19 stress 0.09152179
## Run 20 stress 0.09113361
## *** Solution reached
## INFO [2020-08-20 11:43:35] Done.
## INFO [2020-08-20 11:43:35] Saving ...
## INFO [2020-08-20 11:43:37] Supplement Beta plots ...
## INFO [2020-08-20 11:43:37] Done.
## INFO [2020-08-20 11:43:37] Global3...
## [1] ""
## INFO [2020-08-20 11:43:37] No glom ...
## INFO [2020-08-20 11:43:37] Bray ...
##
## mutant sauvage
## 12 12
## INFO [2020-08-20 11:43:37] Done
## INFO [2020-08-20 11:43:37] Unifrac ...
## INFO [2020-08-20 11:43:38] Done
## INFO [2020-08-20 11:43:38] wunifrac ...
## INFO [2020-08-20 11:43:38] Done
##
## #####################
## ##PERMANOVA on BrayCurtis distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("BC.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.5075 0.50751 1.9574 0.06845 0.098901 .
## souche 1 1.4622 1.46217 5.6393 0.19720 0.000999 ***
## Residuals 21 5.4449 0.25928 0.73435
## Total 23 7.4146 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on BrayCurtis distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 1.529137 5.715979 0.2062341 0.001 0.001 **
##
## #####################
## ##PERMANOVA on UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("UF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.14109 0.14109 1.3569 0.04814 0.238761
## souche 1 0.60653 0.60653 5.8333 0.20693 0.000999 ***
## Residuals 21 2.18352 0.10398 0.74494
## Total 23 2.93114 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.6411514 6.159557 0.2187377 0.001 0.001 **
##
## #####################
## ##PERMANOVA on Weighted UniFrac distances
## #####################
##
## Call:
## adonis(formula = as.formula(paste("wUF.dist ~ Depth +", paste(cov1, collapse = "+"), "+", col)), data = mdata, permutations = 1000)
##
## Permutation: free
## Number of permutations: 1000
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## Depth 1 0.34096 0.34096 4.5057 0.14820 0.004995 **
## souche 1 0.37060 0.37060 4.8974 0.16108 0.006993 **
## Residuals 21 1.58915 0.07567 0.69072
## Total 23 2.30071 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## #####################
## ##pairwisePERMANOVA on Weighted UniFrac distances
## #####################
## pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
## 1 sauvage vs mutant 1 0.4044203 4.691911 0.1757803 0.002 0.002 *
## INFO [2020-08-20 11:43:38] Plotting ...
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004764
## Run 1 stress 0.1004766
## ... Procrustes: rmse 0.0001305564 max resid 0.0003850547
## ... Similar to previous best
## Run 2 stress 0.1004882
## ... Procrustes: rmse 0.0058278 max resid 0.02233927
## Run 3 stress 0.3224833
## Run 4 stress 0.1275006
## Run 5 stress 0.1004883
## ... Procrustes: rmse 0.005792628 max resid 0.02227756
## Run 6 stress 0.1004765
## ... Procrustes: rmse 3.872382e-05 max resid 9.635155e-05
## ... Similar to previous best
## Run 7 stress 0.1282056
## Run 8 stress 0.1004882
## ... Procrustes: rmse 0.005807573 max resid 0.02226059
## Run 9 stress 0.1004764
## ... Procrustes: rmse 5.338605e-05 max resid 0.0001382309
## ... Similar to previous best
## Run 10 stress 0.1282049
## Run 11 stress 0.1004882
## ... Procrustes: rmse 0.005849562 max resid 0.02246848
## Run 12 stress 0.1278163
## Run 13 stress 0.1384184
## Run 14 stress 0.1004882
## ... Procrustes: rmse 0.005814917 max resid 0.02231782
## Run 15 stress 0.1004764
## ... Procrustes: rmse 1.65062e-05 max resid 4.742263e-05
## ... Similar to previous best
## Run 16 stress 0.1004882
## ... Procrustes: rmse 0.00581589 max resid 0.02233953
## Run 17 stress 0.1004764
## ... Procrustes: rmse 2.533215e-05 max resid 7.100506e-05
## ... Similar to previous best
## Run 18 stress 0.1316327
## Run 19 stress 0.1282053
## Run 20 stress 0.1004882
## ... Procrustes: rmse 0.005813854 max resid 0.02230593
## *** Solution reached
## Square root transformation
## Wisconsin double standardization
## Run 0 stress 0.1004882
## Run 1 stress 0.3812595
## Run 2 stress 0.2427133
## Run 3 stress 0.1322745
## Run 4 stress 0.1278118
## Run 5 stress 0.1004882
## ... Procrustes: rmse 2.675765e-05 max resid 7.361028e-05
## ... Similar to previous best
## Run 6 stress 0.1322881
## Run 7 stress 0.1004882
## ... New best solution
## ... Procrustes: rmse 1.017588e-05 max resid 2.074624e-05
## ... Similar to previous best
## Run 8 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 0.005816595 max resid 0.02230714
## Run 9 stress 0.1004764
## ... Procrustes: rmse 1.25171e-05 max resid 3.671702e-05
## ... Similar to previous best
## Run 10 stress 0.1004884
## ... Procrustes: rmse 0.005829559 max resid 0.02234914
## Run 11 stress 0.1332142
## Run 12 stress 0.1282046
## Run 13 stress 0.3812845
## Run 14 stress 0.1282056
## Run 15 stress 0.1004884
## ... Procrustes: rmse 0.005822553 max resid 0.02230221
## Run 16 stress 0.1004764
## ... New best solution
## ... Procrustes: rmse 1.551794e-05 max resid 4.493506e-05
## ... Similar to previous best
## Run 17 stress 0.1004882
## ... Procrustes: rmse 0.005814098 max resid 0.02231398
## Run 18 stress 0.1004882
## ... Procrustes: rmse 0.005824241 max resid 0.02234095
## Run 19 stress 0.1004764
## ... Procrustes: rmse 1.352306e-05 max resid 3.674424e-05
## ... Similar to previous best
## Run 20 stress 0.1274956
## *** Solution reached
## Run 0 stress 0.1224592
## Run 1 stress 0.1235196
## Run 2 stress 0.147283
## Run 3 stress 0.1235347
## Run 4 stress 0.169011
## Run 5 stress 0.1228687
## ... Procrustes: rmse 0.009360572 max resid 0.0342429
## Run 6 stress 0.1224592
## ... New best solution
## ... Procrustes: rmse 0.001457613 max resid 0.005158155
## ... Similar to previous best
## Run 7 stress 0.1224592
## ... Procrustes: rmse 0.001546598 max resid 0.005468143
## ... Similar to previous best
## Run 8 stress 0.1224592
## ... Procrustes: rmse 0.001544245 max resid 0.005459348
## ... Similar to previous best
## Run 9 stress 0.1234827
## Run 10 stress 0.1224592
## ... Procrustes: rmse 7.37536e-05 max resid 0.0002022854
## ... Similar to previous best
## Run 11 stress 0.1235046
## Run 12 stress 0.1235044
## Run 13 stress 0.1235043
## Run 14 stress 0.1224029
## ... New best solution
## ... Procrustes: rmse 0.01278869 max resid 0.04410855
## Run 15 stress 0.1235043
## Run 16 stress 0.1235195
## Run 17 stress 0.1235043
## Run 18 stress 0.1237818
## Run 19 stress 0.1664051
## Run 20 stress 0.1235043
## *** No convergence -- monoMDS stopping criteria:
## 19: stress ratio > sratmax
## 1: scale factor of the gradient < sfgrmin
## Run 0 stress 0.09936821
## Run 1 stress 0.09937007
## ... Procrustes: rmse 0.001310072 max resid 0.003921009
## ... Similar to previous best
## Run 2 stress 0.1012286
## Run 3 stress 0.08369252
## ... New best solution
## ... Procrustes: rmse 0.08719136 max resid 0.3717686
## Run 4 stress 0.08804483
## Run 5 stress 0.08804581
## Run 6 stress 0.1031551
## Run 7 stress 0.08369227
## ... New best solution
## ... Procrustes: rmse 6.634335e-05 max resid 0.0002523386
## ... Similar to previous best
## Run 8 stress 0.09856258
## Run 9 stress 0.1031617
## Run 10 stress 0.1031506
## Run 11 stress 0.09856061
## Run 12 stress 0.2121487
## Run 13 stress 0.1031548
## Run 14 stress 0.08804426
## Run 15 stress 0.09856277
## Run 16 stress 0.08804462
## Run 17 stress 0.09856058
## Run 18 stress 0.1174749
## Run 19 stress 0.08483097
## Run 20 stress 0.0848319
## *** Solution reached
## INFO [2020-08-20 11:43:39] Done.
## INFO [2020-08-20 11:43:39] Saving ...
## INFO [2020-08-20 11:43:41] Supplement Beta plots ...
## INFO [2020-08-20 11:43:41] Done.
## INFO [2020-08-20 11:43:41] Finish